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import Gaffer
import GafferML

Gaffer.Metadata.registerNode(

	GafferML.TensorToImage,

	plugs = {

		"tensor" : {

			"description" :
			"""
			The input tensor to be turned into an image. Typically this would be connected
			to the output of an Inference node that is doing image processing.
			""",

			"plugValueWidget:type" : "",
			"nodule:type" : "GafferUI::StandardNodule",

		},

		"channels" : {

			"description" :
			"""
			The names to give to the channels in the output image. These
			channels are unpacked from the tensor in the order in which they are
			specified. For example, an order of `[ "B", "G", "R" ]` might be
			needed for use with models trained on images using OpenCV
			conventions. An empty channel name may be used to skip a channel
			when unpacking.
			""",

		},

		"interleavedChannels" : {

			"description" :
			"""
			Indicates that the channels are interleaved in the input tensor, in
			which case they will be deinterleaved when converting to the output
			image. Whether or not channels are interleaved will depend on the
			model from which the tensor is obtained.
			""",

		},

		"out" : {

			"description" :
			"""
			The output image.
			""",

		},

	}
)
